Grover Model Research Articles

Accuracy of the Financial Distress Model in Transportation Firms: Altman, Springate and Grover Model

This study analyzes the comparison of financial distress prediction models in transportation firms listed on the Indonesia Stock Exchange during 2018–2020. Using a quantitative approach and secondary data, the study focuses on a sample of 7 firms selected through purposive sampling from a population of 27 firms. The analysis evaluates the Altman, Springate, and Grover models for their accuracy and error rates. Results show that the Altman and Springate models exhibit identical accuracy and error rates, with an accuracy level of 42.56% and an error rate of 57.14%. In contrast, the Grover model demonstrates superior performance with an accuracy level of 85.71% and an error rate of 14.29%. These findings establish the Grover model as the most accurate tool for predicting financial distress among the models tested. Managerial implications highlight the importance of utilizing accurate financial distress prediction models to identify potential financial issues early. This can aid transportation firms in improving financial management and ensuring operational sustainability. By adopting effective prediction models like the Grover model, firms can strengthen their capacity to mitigate risks associated with financial instability

Prediksi Kebangkrutan dengan Model Grover, Altaman Z-Score, Springate, Zmijewski, Olhson dan Fulmer: pada Perusahaan Pertambangan di Bursa Efek Indonesia (BEI) Tahun 2017 – 2021

The aim of this research is to determine (1) predictions of bankruptcy for mining companies listed on the Indonesia Stock Exchange using the Altman Z-Score, Springate, Fulmer, Ohlson, Zmijewski and Grover models from 2017 to 2021, and (2) to determine the level of accuracy six models in predicting bankruptcy. The sampling method used purposive sampling with several criteria which resulted in 37 samples of mining companies during 5 years of data observation. The research results show that, using the Altman model and the Ohlson model, it is estimated that 12 companies will experience bankruptcy. Springate model shows that 28 companies will go bankrupt. Meanwhile, the Fulmer and Zmijewski model predicts 31 companies will go bankrupt and the Grover model predicts 2 companies will go bankrupt. The Zmijewski model is a model that has the highest level of accuracy in predicting bankruptcy in mining companies, namely with an accuracy level of 98%.

Post-Pandemic Financial Distress Analysis: A Study of Retailer and Tourism Companies Listed in Bursa Malaysia Using the Grover Model

A corporation enters financial distress when its cash flows are insufficient to cover its debts to stakeholders, both financial and non-financial. When the Coronavirus Disease 2019 (COVID-19) pandemic struck the world and caused lockdowns that impacted many businesses, particularly those in the tourism and retail industries, the likelihood of financial distress increased. Thus, the Grover model is used to determine and analyse the financial distress prediction in retail and tourist companies listed on Bursa Malaysia. The Grover model categorises companies as financially distressed with a G-Score equal to or less than -0.02. Meanwhile, the G-Score for companies in the safe zone is equal to or greater than 0.01. Four companies were chosen, and a sample of data was collected from 2017 to 2021. The findings showed that during the COVID-19 pandemic from 2020 to 2021, three companies were under financial distress, especially those in the tourism and hospitality sectors. Meanwhile, retailer companies, especially those in food manufacturing, were not affected by this pandemic.

Analisis Tingkat Kebangkrutan pada PT Patra Trading Jakarta

This research aims to determine the prediction of the bankruptcy level of PT. Patra Trading uses financial report data from 2021 to 2023. The method used in this research is Quantitative Descriptive non-Statistical. This research uses secondary data originating from the company PT Patra Trading in the form of the company's annual financial report. The population used is a subsidiary of PT Pertamina Patra Niaga. The sampling technique used in this research is Purposive Sampling for taking samples from the population based on certain criteria. The benchmarks for this bankruptcy prediction analysis consist of several ratios; (1) working capital to total asset; (2) rentaint to total asset; (3) profit before interest and tax to total assets; (4) book value equity to book value liquiditas; (5) profit before tax on current liabilities; (6) sales to total assets: (7) return of assets; (8) debt ratio; (9) current ratio. The results of the analysis using the Altman Z-Score, Springate, Zmijewski, Grover model on PT Patra Trading, provide different research results, where two of them say that PT Patra Trading is in the healthy category, namely in the Zmijewski model the final figure for this model is below 0 and the Grover model final figure is above 0.001. The Springate model says that PT. Patra Trading is still in the bankruptcy category where the final figure is below the standard, namely 0.862. Finally using the Altman Z Score model at PT. Patra Trading is in the gray area.

Analysis Of Bankruptcy Prediction Using Springate, Zmijewski, Grover. And Altman Z-Score Models On Retail Companies Listed On The Indonesia Stock Exchange

The retail sector plays a significant role and contributes greatly to the national economy. The slowdown in domestic economic growth and decreased consumer purchasing power have significantly impacted the retail business in Indonesia. This is evident from the high number of retail companies declared bankrupt and many large retail companies closing their stores. The aim of this study is to assess the health level of retail companies, determine the differences in scores among the research models, and evaluate the accuracy of the Springate, Zmijewski, Grover, and Altman models. This research is a quantitative study with a descriptive approach. The research objects are retail companies listed on the Indonesia Stock Exchange during the period 2019-2021. Sampling technique used is purposive sampling. The results of this study indicate that the highest accuracy level is achieved by the Grover model with an accuracy of 77.7%, followed by the Zmijewski model with an accuracy level of 72.2%. Meanwhile, the Springate and Altman models have the same accuracy level of 66.6%.

Comparative analysis of models (Altman, Grover, Zmijewski, Springate) in predicting company bankruptcy potential in the non-cyclical consumer sector

This research aims to conduct a comparative analysis of models (Altman, Grover, Zmijewski, Springate) in predicting potential bankruptcy for companies in the non-cyclical consumer sector listed on the IDX in 2020-2022. The main aim of this research is to test the results of the comparison of four models and test the accuracy of the prediction model in predicting bankruptcy. The data in this research is 240 data, namely 3 years of timeseries data and 80 companies using purposive sampling techniques according to certain criteria. Data analysis used the Kruskal Wallis Difference Test and Accuracy Level Test. The research results show that the Altman, Grover, Zmijewski, and Springate models have significant differences in results in predicting bankruptcy and the accuracy level test produces the Grover model with the best level of accuracy in predicting bankruptcy in non-cyclical consumer sector companies in 2020-2022.

Business Feasibility Analysis for Investors in Telecommunications Companies for 2017-2022 Period Using the Financial Distress Model

This study analyzes the financial viability of Indonesian telecommunications companies from 2017 to 2022 using financial distress models. The research focuses on comparing the Altman Z-Score and Grover models to predict financial distress, evaluating their effectiveness in providing insights for investors. The telecommunications sector in Indonesia has seen rapid growth, driven by increased smartphone penetration, internet access, and the adoption of 5G technology. However, this growth is accompanied by challenges such as intense competition, economic instability, and regulatory changes, which pose risks of financial distress.Financial distress in telecommunications companies is assessed through key financial ratios, examining liquidity, leverage, and profitability. The study utilizes historical financial data from leading telecommunications companies, applying the Zmijewski and Grover models to predict bankruptcy and assess financial health. Results indicate significant variations in financial performance across companies, with some showing stability and others fluctuating between healthy and distressed states.The research identifies critical financial ratios used in performance evaluation and highlights the relevance of each method. Findings suggest that while both models are valuable, the Zmijewski model demonstrates higher accuracy (93%) compared to the Grover model (77%), making it more reliable for predicting financial distress. This study contributes to better financial management practices in the telecommunications sector, offering investors a comprehensive tool for making informed decisions and mitigating risks.

THE EFFECT OF FINANCIAL DISTRESS AND PROFITABILITY ON EARNING MANAGEMENT

This research aims to provide empirical evidence that is related to the effect of financial distress and profitability on earnings management. Earnings management is measured by the Jones Modified Model. Financial distress is measured by the Grover Model. Profitability is proxied by profit margin, return on assets, return on equity, and earnings per share. The research sample of this study is a consumer goods industry which is listed in Indonesia Stock Exchange 2015-2017. Purposive sampling was used as a sampling method and obtained 108 samples. The analysis technique used is panel regression analysis with Stata 14. The results of this study indicate that each of financial distress, return on equity, and earnings per share has an effect on earnings management while profit margin and return on assets do not affect earnings management.

Logistic Regression Model for Evaluating Performance of Construction, Technology and Property-Based Companies in Malaysia

Financial distress refers to a situation where a company is facing significant financial difficulties or is at risk of insolvency. Being able to anticipate financial distress can help companies take proactive measures to address underlying problems, improve their financial health, and avoid bankruptcy. Likewise, investors can use such predictions to make informed decisions about their investments. The aim is to examine the significant factors of financial distress and accuracy model that can be effectively used in practice to analyse the financial distress of a construction, technology, and property-based companies. In this study, the Altman, Springate, Grover and Zmijerski model is used to classify the financial distress among construction, technology, and property-based companies in Bursa Malaysia Market Exchange during the 2017 to 2021. The models employ the logistic regression method to predict multiple financial ratios simultaneously to assess a company’s financial distress. The result shows that the most significant financial ratio for Altman Z-Score model is X1, and X4 followed by Springate model are X1, X5 and X6 and lastly, Zmijewski are ROA and DR. It also found that 100% accuracy of the Grover model suggested method has an acceptable efficiency to predict financial distress followed by Altman, Springate and Zmijewski model.

Evaluating the efficacy of financial distress prediction models in Malaysian public listed companies

This research critically examines the precision of financial distress prediction models, with a particular focus on their applicability to Malaysian publicly listed companies under Practice Note 17 (PN17) from 2017 to 2021. Financial distress, defined as the imminent risk of bankruptcy evidenced by an inability to satisfy creditor demands, presents a significant challenge in corporate finance management. The study underscores the necessity of an efficient prediction model to strategize preemptive measures against financial crises. Unlike prior research, which predominantly compared prediction models without assessing their accuracy, this study incorporates an accuracy analysis to discern the most effective model. Utilizing the Grover and Zmijerski models, it assesses whether companies listed under PN17 are experiencing financial distress. A noteworthy finding is the substantial correlation between the return on assets (ROA) and the prediction of financial distress in these companies. Furthermore, the Grover model demonstrates a remarkable 100% accuracy rate, indicating its exceptional efficiency in forecasting financial distress. This research not only contributes to the existing body of knowledge on financial distress prediction but also offers practical insights for companies and stakeholders in the Malaysian financial market.

Analysis Of Potential Bankruptcy In Blue Bird Tbk And Transindo Utama Tbk Using Springate And Grover Models

Financial statement analysis is very important for companies. By analysing financial statements, the potential for bankruptcy can be known from the start. Financial statements serve as a source of information for creditors, investors, employees, and other parties in need.In industry 4.0, everything is online, even transport. The object of this research is PT Blue Bird Tbk. and PT Transindo Utama Tbk. The purpose of this study is to determine the company's financial performance in the last five years and potential bankruptcy. The analysis method in this study is the Springate model and the Grover model.The conclusion of this study is that using the Springate model, PT Blue Bird Tbk. in 2018 shows a healthy financial condition, while in 2019-2022 it shows potential bankruptcy. PT Transindo Utama Tbk. in 2018, 2019, 2020, 2022 shows potential bankruptcy, while in 2021 the financial condition is healthy. Then using the Grover model, PT Blue Bird Tbk. shows no potential bankruptcy. PT Transindo Utama Tbk. shows potential bankruptcy in 2018-2020, while in 2021 and 2022 the financial condition is healthy.

Financial distress predictions with Altman, Springate, Zmijewski, Taffler and Grover models

Several models have been developed to predict financial difficulties and corporate bankruptcy. In this research various models were employed, including the Altman model (referred to as the Z-Score), the Springate model (known as the S-Score), the Zmijewski model (designated as the X-Score), and the Grover model (referred to as the G-Score). These techniques serve the purpose of evaluating the likelihood of encountering financial difficulties, which in turn determines the probability of PT Garuda Indonesia (Persero) Tbk going bankrupt. The study utilized secondary data sourced from financial statements spanning the years from 2020 to 2022. The application of the Altman model for bankruptcy prediction revealed that PT Garuda Indonesia (Persero), Tbk experienced financial distress throughout the period from 2020 to 2022. According to the Springate model, the company was in a state of distress and declared bankruptcy in 2020 and 2022, while 2021 fell into a grey area. The Zmijewski model indicated that the company was on the brink of bankruptcy, with financial difficulties and a potential risk of bankruptcy within the next three years. Grover's model predicted bankruptcy for the company in 2020 and 2022, but indicated safety in 2021. Notably, the Taffler model emerged as the most accurate in forecasting bankruptcy, boasting a 100% accuracy rate with no errors. Meanwhile, the Zmijewski model achieved an 81.25% accuracy rate with an error rate of 18.75%, and the Springate model exhibited the lowest accuracy in bankruptcy prediction, scoring only 12.50% accuracy with an error rate of 87.50%.

Prediction and Comparison of Bankruptcy Models in Banking Sector Companies in Indonesia

The Covid-19 pandemic, coupled with the economic recession from 2019 to 2022, has adversely impacted various industries, leading to bankruptcies. Among the affected sectors, the banking industry faced significant challenges, experiencing disruptions in credit processes and fund distribution due to diminished purchasing power. This situation poses a severe threat to the banking sector, necessitating the monitoring of financial conditions through bankruptcy analysis as an early warning system for company performance. This study investigates potential differences in the results of financial distress prediction models—Altman, Springate, Zmijewski, Grover, and Ohlson—during the abnormal conditions of the Covid-19 pandemic. Using purposive sampling of banking companies listed on the IDX from 2019 to 2022, the study divided the samples into financial distress and non-financial distress categories. Analysis involved tests for multicollinearity assumptions, logistic regression, and accuracy/error rate calculations. The findings reveal variations in the predictive abilities of the Altman, Springate, Zmijewski, Grover, and Ohlson models. The Grover model emerged as the most accurate, with a 60% accuracy rate in predicting bankruptcy, while Altman, Springate, and Zmijewski models demonstrated low predictive values (0%, 0%, and 15% accuracy, respectively). The simplicity of the Grover model's measurement indicators, incorporating capital adequacy, EBIT, and ROA, offers a comprehensive view of bankruptcy prediction ratios. Moreover, stringent internal risk analysis and external factors, such as regulatory interventions, contribute to keeping banks resilient amid global economic crises. The research suggests that banking companies can benefit from employing the Grover method for bankruptcy analysis as part of their future anticipation strategy.

Analysis Of Financial Distress In The Covid 19 Pandemic Era

This research aims to find out (1) find out whether there are differences in scores between the Altman, Springate, Zmijeski, and Grover models in predicting Financial Distress, (2) find out which companies remain healthy (green area) or do not experience Financial Distress and which companies which ones are unhealthy (red are) or experiencing Financial Distress. This research uses data from company financial reports with Special Notifications published on the Indonesia Stock Exchange website for 2020-2021. The sampling technique used purposive samples to obtain 10 companies that could be used as research. The results of this research show that the Altman model and the Grover model have the same score, namely an accuracy level of 40% and an error type of 20%, followed by the Zmijewski model with an accuracy rate of 30% and an error type of 30% and the Springate model an accuracy rate of 10% and an error type of 30%.

Analisis Financial Distress pada Perusahaan Konstruksi BUMN Karya

This study aims to measure the performance and financial health of the BUMN Karya Construction Company based on an analysis of financial reports from 2019 to 2022. This research is also intended to determine increases or decreases in financial performance and even financial distress. This study examines financial problems in 5 (five) BUMN Karya companies, including PT. Adhi Karya, PT. Waskita Karya, PT. Wijaya Karya, PT. Pembangunan Perumahan and PT. Hutama Karya. Financial performance measurement analysis based on Financial Ratio Analysis and Discriminant or Financial Distress Prediction Models include the Altman Z-score Model, the Springate Model, the Grover Model and the Zmijewski Model. The research will be measured based on the individual analysis of each company and the comparative analysis of the 5 (five) BUMN Karya. The results of the research based on analysis of financial ratios and 4 (four) financial distress models obtained values of 50% to 100%, so that it can be concluded that 5 (five) BUMN Karya companies are experiencing financial distress or financial difficulties and are on the verge of bankruptcy. While the research results are based on the calculation of financial ratios where the average state-owned company works, the majority of its assets come from debt but still have sufficient cash to pay off its debts. The conclusion from the results of this study can be used as an early warning related to the company's financial condition, so that it can be planned to develop company policies and strategies for better financial performance and avoid financial distress.

Mengukur Kondisi Kesehatan Keuangan Perusahaan Sub- Industri Farmasi Saat Pandemi Covid-19

This research is intended to obtain information regarding the financial health of companies in the pharmaceutical sub-industry (code F211) on the Indonesia Stock Exchange (BEI) during the period during the Covid-19 pandemic (2020-2022). To detect whether a company is experiencing financial health problems or its finances are experiencing pressure, a multiple discriminant analysis (MDA) model is used. There are 6 MDA models used, namely: Altman model, Fulmer model, Grover model, Springate model, Taffler model, and Zmijewski model. The type of research carried out includes exploratory descriptive research. The entire population was taken as a sample, namely 11 companies belonging to the pharmaceutical sub-industry (code F211). The research results show that the use of different models produces the same results, but some also produce different results. Generally, the results of analysis of a company experiencing very severe financial pressure using several models will produce the majority of the same results. Likewise, if an analysis is carried out on a company with a very healthy financial condition using several models it will produce the same results as the majority of the models used. The research results show that there are 4 companies that need to pay attention to their financial health, namely: PT Indofarma Tbk. (INAF), PT Kimia Farma Tbk. (KAEF), PT Phapros Tbk. (PEHA), and PT Pyridam Farma Tbk. (PYFA). There are 2 companies that are indicated to have very healthy financial conditions, namely: PT Kalbe Farma Tbk. (KLBF) and PT Industri Jamu and Pharmacy Sido Muncul Tbk. (SIDO).

BANKRUPTCY PREDICTION WITH THE ALTMAN Z-SCORE, SPRINGATE, ZMIJEWSKI, AND GROVER MODELS

Purpose – This study aims to predict bankruptcy towards food and beverage subsector and finding the most accurate analysis model among the Altman Z-Score, Springate, Zmijewski, and Grover
 Design/methodology/approach – This analysis uses quantitative descriptive method using Ms. Excel, then performed Kruskal Wallis and accuracy test using IBM-SPSS
 Findings – The results by three-year average calculation using Altman Z-Score model shows that 2 out of 21 companies have the potential to go bankrupt, Springate model has 9 out of 21 companies, Zmijewski model has 3 out of 21 companies, and based on Grover model there are 2 out of 21 companies. The results of the hypothesis test showed that there were differences between the models and the Zmijewski model is the most accurate for food and beverage subsector companies.
 Originality – This study uses the most accurate analysis model among the Altman Z-Score, Springate, Zmijewski, and Grover by using 2017-2019 financial report data published on Indonesia Stock Exchange (IDX)
 Keywords: Bankruptcy, Altman Z-Score, Springate, Zmijewski, Grover
 Paper Type Research Result

Predicting Financial Distress in Indonesia, Malaysia, and Thailand Transportation and Tourism Companies: a Comparative Analysis of Altman, Springate, Ohlson, Zmijewski, and Grover Models

This study delves into the world of predictive models, aiming to anticipate financial distress in the tourism and transportation sectors across Indonesia, Thailand, and Malaysia. The research scrutinizes five distinguished models – Altman, Springate, Ohlson, Zmijewski, and Grover – to illuminate the evolving financial distress patterns. The research starts by precisely evaluating these financial models' forecasting capabilities, leveraging an extensive dataset from 2018 to 2022. This thorough assessment gauges the accuracy inherent in each model, unveiling their performance within the unique economic landscapes of these three nations.The research identifies models that consistently hit the mark in accuracy through the prism of cross-country and multi-year evaluations. The findings underscore the predictive prowess of each model in foreseeing financial distress across the selected companies. However, these models exhibit distinct accuracy rates in the grand scheme and when zooming into individual countries. The Grover Model is the champion across the chosen countries, boasting the highest accuracy scores. In country-specific analyses, Zmijewski's predictive prowess shines as the most reliable for Malaysia and Thailand. Meanwhile, Grover steals the spotlight in Indonesia by outperforming its counterparts in predictive accuracy.

ANALISIS PERBANDINGAN MODEL GROVER, ZMIJEWSKI, DAN SPRINGATE DALAM MEMPREDIKSI FINANCIAL DISTRESS PADA MASA PANDEMI COVID-19 (STUDI KASUS PADA PERUSAHAAN SUB SEKTOR HOTEL, RESTORAN, DAN PARIWISATA YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2020 & 2021)

This study aims to determine and analyze whether there are differences in accuracy between the Grover, Zmijewski, and Springate models in predicting financial distress during the Covid-19 pandemic in tourism companies in the hotel, restaurant and tourism sub-sectors listed on the Indonesia Stock Exchange. The sampling technique used purposive sampling and obtained a total of 40 company samples. The type of research used is descriptive quantitative to compare the accuracy of 3 models in predicting financial distress in hotel, restaurant and tourism sub-sector companies. The data analysis used in this research is descriptive analysis and accuracy test. The results of this study indicate that there are differences between the three prediction models. The Zmijewski model has the highest accuracy level of 87.5% with an error I level of 12.5%, second is occupied by the Grover model with an accuracy level of 82.5% with an error I level of 17.5% and the third is the Springate model with the lowest accuracy level of 20% and an error I level of 80%.

ANALYSIS OF FINANCIAL DISTRESS IN BROADCASTING SUB-SECTOR COMPANIES LISTED ON THE INDONESIA STOCK EXCHANGE

Financial distress, also known as financial difficulties, refers to a situation in which a company experiences unhealthy financial conditions or a crisis. It typically starts with increasing liquidity pressure and continues with a decrease in the value of company assets, ultimately leading to bankruptcy. Bankruptcy poses a significant risk for companies; however, this risk can be minimized or prevented through the use of bankruptcy detection models or tools. The purpose of this study was to assess the financial distress of companies in the Broadcasting Sub-Sector listed on the Indonesia Stock Exchange during the period of 2017-2021. The Springate, Zmijewski, and Grover models were employed for this analysis. The study's population comprised companies in the Broadcasting Sub-Sector listed on the Indonesia Stock Exchange during the specified period, amounting to 5 companies. A research sample of 4 companies was selected using a purposive sampling technique. The findings indicate that according to the Springate model, two companies, namely PT. MDIA Tbk with a rate of 80% and PT. VIVA Tbk with a level of 100%, were in a state of financial distress. The Zmijewski model identified PT VIVA Tbk as the only company in financial distress with a level of 80%. Additionally, based on the Grover model, PT. VIVA Tbk exhibited a financial distress level of 40%.